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AI Agents

Overview

AIDDDMAP employs a sophisticated multi-agent system where specialized AI agents collaborate to handle different aspects of data management, curation, and marketplace operations. Each agent has specific responsibilities and capabilities, coordinated by a central Steering Librarian.

Agent Architecture

interface BaseAgent {
  id: string;
  type: AgentType;
  status: "idle" | "processing" | "error";
  capabilities: string[];
  process(input: any): Promise<any>;
  handleError(error: Error): void;
}

enum AgentType {
  FINDER = "finder",
  CURATOR = "curator",
  VALUATOR = "valuator",
  TREND_ANALYZER = "trend_analyzer",
  PUBLISHER = "publisher",
  QUALITY_ASSESSOR = "quality_assessor",
  LIBRARIAN = "librarian",
}

Core Agents

1. DataFinder AI

Purpose: Discovers and locates relevant datasets based on user criteria.

interface DataFinderAgent extends BaseAgent {
  searchCriteria: {
    keywords: string[];
    dataTypes: string[];
    dateRange?: {
      start: Date;
      end: Date;
    };
  };

  findDatasets(criteria: SearchCriteria): Promise<Dataset[]>;
  rankResults(datasets: Dataset[]): Promise<RankedDataset[]>;
  suggestRelated(dataset: Dataset): Promise<Dataset[]>;
}

Example Usage:

const finder = new DataFinderAgent({
  type: AgentType.FINDER,
  capabilities: ["semantic_search", "similarity_ranking"],
});

const datasets = await finder.findDatasets({
  keywords: ["health", "fitness"],
  dataTypes: ["timeseries", "metrics"],
});

2. DataCurator AI

Purpose: Prepares and enhances datasets for marketplace listing.

interface DataCuratorAgent extends BaseAgent {
  supportedFormats: string[];
  enhancementCapabilities: string[];

  standardizeFormat(data: any): Promise<any>;
  enrichMetadata(dataset: Dataset): Promise<Dataset>;
  validateSchema(dataset: Dataset): Promise<ValidationResult>;
}

Example Usage:

const curator = new DataCuratorAgent({
  type: AgentType.CURATOR,
  capabilities: ["format_conversion", "metadata_enhancement"],
});

const enhancedDataset = await curator.enrichMetadata(rawDataset);

3. ValuationAI

Purpose: Determines fair market value for datasets.

interface ValuationAgent extends BaseAgent {
  marketData: MarketMetrics;

  calculateValue(dataset: Dataset): Promise<ValuationResult>;
  analyzeDemand(dataset: Dataset): Promise<DemandMetrics>;
  suggestPricing(dataset: Dataset): Promise<PricingStrategy>;
}

interface ValuationResult {
  estimatedValue: number;
  confidence: number;
  factors: {
    quality: number;
    uniqueness: number;
    demand: number;
    completeness: number;
  };
}

4. TrendAnalysis AI

Purpose: Analyzes market trends and provides insights.

interface TrendAnalysisAgent extends BaseAgent {
  timeframe: "daily" | "weekly" | "monthly";

  analyzeMarketTrends(): Promise<MarketTrends>;
  predictDemand(dataset: Dataset): Promise<DemandPrediction>;
  generateInsights(data: MarketData): Promise<MarketInsights>;
}

5. Publishing AI

Purpose: Manages the dataset publication process.

interface PublishingAgent extends BaseAgent {
  publishingQueue: Dataset[];

  publishDataset(dataset: Dataset): Promise<PublishResult>;
  trackStatus(datasetId: string): Promise<PublishStatus>;
  handleErrors(error: PublishError): Promise<Resolution>;
}

6. Data Quality Assessment AI

Purpose: Evaluates and validates dataset quality.

interface QualityAssessmentAgent extends BaseAgent {
  metrics: QualityMetrics[];

  assessQuality(dataset: Dataset): Promise<QualityReport>;
  validateIntegrity(dataset: Dataset): Promise<ValidationResult>;
  suggestImprovements(dataset: Dataset): Promise<Improvements[]>;
}

interface QualityReport {
  score: number;
  metrics: {
    completeness: number;
    accuracy: number;
    consistency: number;
    timeliness: number;
  };
  issues: Issue[];
  recommendations: string[];
}

7. Steering Librarian

Purpose: Coordinates all agents and maintains system knowledge.

interface SteeringLibrarian extends BaseAgent {
  agents: BaseAgent[];
  knowledgeBase: KnowledgeBase;

  coordinateAgents(task: Task): Promise<void>;
  updateKnowledge(data: any): Promise<void>;
  optimizeWorkflow(workflow: Workflow): Promise<OptimizedWorkflow>;
}

Agent Communication

Agents communicate through an event-driven system:

interface AgentMessage {
  from: string;
  to: string;
  type: MessageType;
  payload: any;
  timestamp: Date;
}

enum MessageType {
  REQUEST = "request",
  RESPONSE = "response",
  ERROR = "error",
  STATUS_UPDATE = "status_update",
}

Example of inter-agent communication:

// DataFinder requesting quality assessment
await messageBus.send({
  from: "finder_agent",
  to: "quality_agent",
  type: MessageType.REQUEST,
  payload: {
    action: "assess_quality",
    dataset: foundDataset,
  },
});

Agent Deployment

Agents can be deployed through the UADM:

interface AgentDeployment {
  agent: BaseAgent;
  config: UADMConfig;
  resources: ResourceRequirements;
}

const deployment = await uadm.deployAgent({
  agent: new DataFinderAgent(),
  config: {
    hardwareAbstraction: {
      protocols: ["http", "websocket"],
      deviceTypes: ["server"],
    },
    security: {
      encryption: true,
      permissions: ["read_data", "search_index"],
    },
  },
});

Best Practices

  1. Error Handling
try {
  await agent.process(data);
} catch (error) {
  await agent.handleError(error);
  await SteeringLibrarian.notifyError(error);
}
  1. Resource Management
const resources = await agent.checkResources();
if (resources.available) {
  await agent.process(data);
} else {
  await agent.queueTask(data);
}
  1. Performance Optimization
// Use caching for frequent operations
const cachedResult = await cache.get(operationKey);
if (cachedResult) {
  return cachedResult;
}

Integration Examples

1. IDAT Integration

// Drag-and-drop agent to canvas
idat.onAgentDrop(async (agent: BaseAgent, position: Position) => {
  const node = await idat.createAgentNode(agent, position);
  await SteeringLibrarian.registerNode(node);
});

2. Marketplace Integration

// Publishing workflow
const workflow = new AgentWorkflow()
  .addStep(qualityAgent.assessQuality)
  .addStep(valuationAgent.calculateValue)
  .addStep(publishingAgent.publishDataset);

await workflow.execute(dataset);

Next Steps